论文标题
Akhcrnet：使用深度学习的孟加拉语手写角色识别
AKHCRNet: Bengali Handwritten Character Recognition Using Deep Learning
论文作者
论文摘要
我提出了一种最先进的神经体系结构解决方案，用于孟加拉字母的手写角色识别，复合字符以及数字数字，仅在11个时代就达到了最先进的精度为96.8％。 Chatterjee，Swagato等人之前也做过类似的工作。但是他们在大约47个时期的精度达到了96.12％。考虑到包含Resnet 50模型的重量，该模型是50层残留网络，该论文中使用的深神经结构非常大。与以前的任何工作相比，该拟议的模型可实现更高的准确性。 RESNET50是一个在Imagenet数据集上训练的良好模型，但是我提出了一个HCR网络，该网络是在没有“合奏学习”的孟加拉语角色训练的，该网络可以超越先前的体系结构。
I propose a state of the art deep neural architectural solution for handwritten character recognition for Bengali alphabets, compound characters as well as numerical digits that achieves state-of-the-art accuracy 96.8% in just 11 epochs. Similar work has been done before by Chatterjee, Swagato, et al. but they achieved 96.12% accuracy in about 47 epochs. The deep neural architecture used in that paper was fairly large considering the inclusion of the weights of the ResNet 50 model which is a 50 layer Residual Network. This proposed model achieves higher accuracy as compared to any previous work & in a little number of epochs. ResNet50 is a good model trained on the ImageNet dataset, but I propose an HCR network that is trained from the scratch on Bengali characters without the "Ensemble Learning" that can outperform previous architectures.
